Discovering Early-Stage Gas Generation Kinetics Enables Thermal Runaway Early Warning in Lithium-Ion Batteries.

IF 14.1 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Advanced Science Pub Date : 2026-09-01 Epub Date: 2026-07-09 DOI:10.1002/advs.76433
Jiabo Zhang, Qianzhen Guo, Shuaiqi Liu, Shuai Li, Xianghao Kong, Dong Han, Zhen Huang
{"title":"Discovering Early-Stage Gas Generation Kinetics Enables Thermal Runaway Early Warning in Lithium-Ion Batteries.","authors":"Jiabo Zhang, Qianzhen Guo, Shuaiqi Liu, Shuai Li, Xianghao Kong, Dong Han, Zhen Huang","doi":"10.1002/advs.76433","DOIUrl":null,"url":null,"abstract":"<p><p>Early warning of thermal runaway (TR) in lithium-ion batteries remains constrained, as conventional indicators emerge after irreversible failure. Here, we establish a novel framework to advance gas-based early warning by uncovering the earliest chemically driven processes preceding heat accumulation (HA). Operando gas measurements reveal a previously unrecognized chemical activation (CA) stage prior to HA, where coupled electrolyte-electrode reactions initiate at 60 <math> <mrow><msup><mrow></mrow> <mo>∘</mo></msup> <mi>C</mi></mrow> </math> , causing continuous trace gas accumulation without measurable temperature rise. To elucidate the underlying mechanisms, a physics-informed gas generation kinetics network (GGKNet) is developed by coupling chemical reaction kinetics with gas adsorption-desorption, electrolyte evaporation, and lithium-involved reactions. The framework quantitatively captures species-resolved gas generation across states of charge and reconstructs pathway-resolved kinetics during CA and HA stages. Validations under TR scenarios demonstrate that GGKNet achieves superior prediction of gas onset timing, temporal evolution, and spatial distribution, thereby translating kinetic-level insights into guidance for practical early warning systems.</p>","PeriodicalId":117,"journal":{"name":"Advanced Science","volume":" ","pages":"e76433"},"PeriodicalIF":14.1000,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13348658/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Science","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/advs.76433","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/7/9 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Early warning of thermal runaway (TR) in lithium-ion batteries remains constrained, as conventional indicators emerge after irreversible failure. Here, we establish a novel framework to advance gas-based early warning by uncovering the earliest chemically driven processes preceding heat accumulation (HA). Operando gas measurements reveal a previously unrecognized chemical activation (CA) stage prior to HA, where coupled electrolyte-electrode reactions initiate at 60 ∘ C , causing continuous trace gas accumulation without measurable temperature rise. To elucidate the underlying mechanisms, a physics-informed gas generation kinetics network (GGKNet) is developed by coupling chemical reaction kinetics with gas adsorption-desorption, electrolyte evaporation, and lithium-involved reactions. The framework quantitatively captures species-resolved gas generation across states of charge and reconstructs pathway-resolved kinetics during CA and HA stages. Validations under TR scenarios demonstrate that GGKNet achieves superior prediction of gas onset timing, temporal evolution, and spatial distribution, thereby translating kinetic-level insights into guidance for practical early warning systems.

发现早期气体生成动力学使锂离子电池热失控预警成为可能。
锂离子电池热失控(TR)的早期预警仍然受到限制,因为传统的指标在不可逆转的故障后才会出现。在这里,我们建立了一个新的框架,通过揭示热积累(HA)之前最早的化学驱动过程来推进基于气体的早期预警。Operando气体测量显示在HA之前有一个以前未被认识到的化学活化(CA)阶段,其中耦合的电解质-电极反应在60°C $^{\circ}{\rm C}$时开始,导致持续的微量气体积累而没有可测量的温升。为了阐明潜在的机制,通过将化学反应动力学与气体吸附-解吸、电解质蒸发和锂相关反应耦合起来,建立了一个物理知情的气体生成动力学网络(GGKNet)。该框架定量捕获了跨电荷状态的物种分解气体生成,并重建了CA和HA阶段的途径分解动力学。TR情景下的验证表明,GGKNet在天然气起生时间、时间演化和空间分布方面取得了较好的预测效果,从而将动力学层面的见解转化为实际预警系统的指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Advanced Science
Advanced Science CHEMISTRY, MULTIDISCIPLINARYNANOSCIENCE &-NANOSCIENCE & NANOTECHNOLOGY
CiteScore
18.90
自引率
2.60%
发文量
1602
审稿时长
1.9 months
期刊介绍: Advanced Science is a prestigious open access journal that focuses on interdisciplinary research in materials science, physics, chemistry, medical and life sciences, and engineering. The journal aims to promote cutting-edge research by employing a rigorous and impartial review process. It is committed to presenting research articles with the highest quality production standards, ensuring maximum accessibility of top scientific findings. With its vibrant and innovative publication platform, Advanced Science seeks to revolutionize the dissemination and organization of scientific knowledge.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书